The almost complete sequencing of the genomes from numerous organisms paved the way for the development and application of new experimental and instrumental techniques which contribute to the understanding of complex biological pathways and networks by providing apparently endless opportunities to generate massive amounts of data. Cell machinery is currently envisaged as an inter-relationship of enzymes, proteins and chemical compounds. However, both a large number of metabolic pathways and enzymes even in well-described pathways still remain unknown. It is therefore necessary to develop further experimental and mathematical methods to reconstruct unknown parts of the networks, to identify genes for missing enzymes and to characterize the kinetic behaviour of those enzymes that have been identified.

The post-genomic era is also characterized by the concept of systems biology. This has gained significant momentum and metabolic research is now being conducted on an integrated and cross-disciplinary platform pulling together its resources from diverse fields such as mathematics, computational biology, bioinformatics, functional genomics and proteomics, and structural biology.

The enormous growth in the computation speed and data storage capability has fuelled new opportunities for both the accumulation of massive amounts of sequence, expression and functional data and the characterization, analysis and comparison of larger biological systems. However, as long as the data quality of the in-put and the resulting modelling data cannot be improved, the chances of success for this young discipline to escape from the verbally overused –omics-sciences are poor.

Systems level investigation of genomic and proteomic scale information requires incomparably higher demands for data quality than in previous decades. Truly integrated databases that deal with heterogeneous data need to be developed to be able to retrieve properties of genes, for kinetics of enzymes, for behaviour of complex networks and for the analysis and modelling of complex biological processes. One perspective of the output can be the investigation of cellular pathways involved in disease biology and targeted by newer molecular therapeutics. The understanding of these processes will assist the development of early diagnosis, prognosis and the prediction of response to individual therapies.

Despite the fast paced global efforts in biological systems research, the current analyses are limited by the lack of available systematic collections of comparable functional enzyme data. Besides its reliability, these data have to provide defined minimum experimental information, they must be available from the literature along with their accepted enzyme names, and must be as comprehensive as possible.

The STRENDA Commission, founded on the 1st ESCEC meeting in 2003, has worked out a number of checklists which are intended to improve the quality of reporting enzyme data and thus to support the comparability of inter alia enzyme kinetics. The commission has also spent much time and effort in the creation of an electronic data submission system which allows authors to deposit their data and to provide an interaction record accession number that can be quoted in publications.

This 3rd ESCEC Symposium, organized by the Beilstein-Institut together with the STRENDA Commission, provided a platform to discuss the checklists. Further suggestions regarding the checklists have been collected and discussed. Questions such as how to organize and store these massive data sets in standard and easily accessible forms have been asked an the first running draft of an data acquisition tool considering the STRENDA guidelines has been presented.

We would like to thank particularly the authors who provided us with written versions of the papers that they presented. Special thanks go to all those involved with the preparation and organization of the symposium, to the chairmen who piloted us successfully through the sessions and to the speakers and participants for their contribution in making this symposium a success.

Frankfurt/Main, August 2008

Carsten Kettner
Martin G. Hicks

A major challenge in biochemistry is to understand the functional genomics of organisms. This is a staggering problem when one considers the fact that almost 40% of the genes in one of the best-understood organisms in the biosphere, Escherichia coli, have no experimentally verified function. In this paper we address the challenge of, and criteria for, assigning protein function in the context of the glutathione (GSH) transferase paralogues encoded in the E. coli genome. The E. coli genome harbors genes encoding nine GSH transferase homologues including YliJ, YncG, Gst, YfcF, YfcG, YghU, SspA and YibF as well as the membrane-bound enzyme YecN. Amazingly, only one of these genes has a reasonably well-defined function and it does NOT encode a protein with GSH transferase activity but rather a transcription factor, stringent starvation protein A, SspA.

Interfacial enzymes present additional challenges in their study compared to enzymes with soluble substrates. Cholesterol oxidase is an interfacial enzyme that transiently associates with lipid membranes to convert cholesterol to cholest-4-en-3-one. As a case study to exemplify the issues that should be considered, we describe our structural and mechanistic understanding of cholesterol oxidase kinetic activity based on X-ray crystal structures and kinetic analysis.

The teaching of enzyme kinetics has been neglected in recent years, with the growth in influence of molecular biology, but its importance has not diminished. Elementary aspects of enzyme inhibition have always been central to the understanding and design of pharmacological agents and pesticides, and both kinetics and metabolism have acquired a new role for making sense of the flood of genome data that has appeared in the past decade. Although at one time it was hoped that sequence analysis alone would be sufficient for deducing phenotypic information from genomic data, it has become clear that it has to be combined with stoicheiometric analysis, knowledge of metabolic networks and analysis of enzyme regulation. Presentation of kinetics in general textbooks has always been very poor, and the decline of specialized teaching has made the inadequacy of these textbooks more serious than it already was in the past.

The HUPO Proteomics Standards initiative has designed and implemented common data reporting and exchange standards to enable the transfer of proteomics data from originator to collaborator to a final public repository immediately prior to publication. This work has been undertaken with extensive community involvement at every stage of the process to ensure that the end product fulfils the users’ needs. The scientific community is already benefiting from this work, with XML formats to exchange and import data into databases, allowing direct access and comparability irrespective of the originating instrumentation. Public repositories allow researchers to access and search published experimental data with the result that reference datasets are becoming available for benchmarking purposes. Collaborations between databases are exposing these datasets to an ever increasing audience and enabling exciting new science to be derived from existing data.

This chapter deals with how one can obtain values of thermodynamic properties – specifically the apparent equilibrium constant K’, the standard molar transformed Gibbs energy change Δ_rG’
°, and the standard molar transformed enthalpy change Δ_rH’
°
for biochemical reactions – and, in particular, for enzyme-catalyzed reactions. In addition to direct measurement, these property values can be obtained in a variety of ways: from thermochemical cycle calculations; from tables of standard molar formation properties; by estimation from property values for a chemically similar reaction or substance; by means of estimation by using a group-contribution method; by combining a known value of the standard molar enthalpy change Δ_rH
°
and an estimated value for the standard molar entropy change Δ_rS
°
in order to obtain the standard molar Gibbs energy change Δ_rG
°
for a given reaction; and by use of computational chemistry.

In biochemical thermodynamics, the apparent equilibrium constants of enzyme-catalyzed reactions have been represented by K’ = K_ref10^npHf(pH), where K_refis a reference chemical reaction, n is the number of hydrogen ions in the reference reaction, and f(pH) is a function of pH that brings in the pKs of the substrates. This equation suggests that hydrogen ions are involved in two different ways in biochemical thermodynamics. If this is true in thermodynamics, it has to be true in kinetics. However, the choice of reference reaction in thermodynamics is arbitrary, and so n cannot be determined from equilibrium measurements. However, when hydrogen ions are consumed in the rate-determining reaction, the experimental limiting velocity of the forward reaction is given by V_fexp = 10^npHV_f. V_f is the limiting velocity in the forward direction when n = 0, or V_f can be calculated from experimental data using V_f = 10^npHV_fexp. V_f brings in the pKs of the enzyme-substrate complex that reacts in the rate-determining reaction. When hydrogen ions are consumed in the rate-determining reaction, the Haldane equation yields K’ = K_ref10^npHf(pH). Since n can be -8 (EC 1.7.7.1), the effects of pH on kinetic and thermodynamic properties can be very large. webMathematica can provide the thermodynamic properties of enzyme-catalyzed reactions that are difficult to calculate and require a database without having Mathematica^®
in a personal computer or knowing how to use it.

There are a number of resources containing enzyme kinetics data. Two widely used databases are BRENDA and SABIO-RK. While these databases contain kinetic constants, the key to ensuring that these resources can be usefully employed in a systems biology environment is in the richness of the metadata associated with these values.

Obvious requirements for these metadata include the environmental conditions, such as pH and temperature, under which these constants were measured. However there are other, more subtle, metadata that must also be captured and recorded along with the kinetic parameters to allow the database to be utilised correctly in modelling and simulation studies. This article describes these metadata and also introduces the KineticsWizard, a data capture tool that allows the experimentalist to specify these data in an intuitive manner.

SABIO-RK is a curated database for the systems biology community containing biochemical reactions and their kinetic properties, the latter being manually extracted from literature sources. This information is crucial for the quantitative understanding of biological systems. Modellers and wet-lab scientists alike require reliable information about reaction kinetics which is normally contained in publications generated worldwide. In SABIO-RK kinetic data are related to reactions, organisms and biological locations. The type of kinetic mechanism and corresponding rate equations are presented together with their parameters and experimental conditions. In order to enable comprehensive understanding, integration and comparison of data it is necessary to provide annotations and links to community resources, such as external databases and ontologies that augment the content and the semantics of the SABIO-RK database entries. In this short paper we will present SABIO-RK and our approach towards integration and annotation of the kinetic data and their respective biochemical context.

The recommendations of the STRENDA Commission (Version 1.2 June 16th, 2006) of standard requirements for reporting enzyme activity data include the proposal that the specification of assay conditions should include any metal salts to be added. They also require the definition of some other parameters which, as will be seen later, may have a bearing on the activity of metal iondependent enzymes. These include assay pH, buffer type and concentrations, and other assay components such as EDTA or dithiothreitol that will coordinate to metal ions.

This chapter is intended to provide a guide to issues that are relevant to the determination of accurate kinetic data for the reactions of metaldependent enzymes. Of particular importance are factors relating to the speciation and availability of metal ions in the assay medium. The interaction of the metal ions in the added metal salts with compounds present in the medium may result in the formation of a number of metal-ligand complexes. These may activate the enzyme to different extents at different rates. In extreme cases, metal ions may be precipitated out of solution and be unavailable to function in enzyme activation. We will further discuss the relevance of the metal ions in modelling the activity of the enzyme in the cell.

The IUBMB Enzyme List is widely used by other databases as a source for avoiding ambiguity in the recognition of enzymes as catalytic entities. However, it was never designed for activities such as pathway tracing, which have become increasingly important in systems biology. This is because it often relies on generic or representative reactions to show the reactions catalysed by enzymes of wide specificity. It is necessary to go to databases such as BRENDA to find further, more detailed, information on what is known about the range of substrates for any particular enzyme. In order to provide a framework for tracing pathways involving any specific enzyme or metabolite, we have created a Reactions Database from the material in the Enzyme List. This allows reactions to be searched by substrate/product and pathways to be traced from any selected starting/seed substrate. An extensive synonym glossary allows searches by any of the alternative names, including accepted abbreviations, by which a chemical compound may be known. This database was necessary for the development of the application Reaction Explorer, which was written in REALbasic to search the Reactions Database and draw metabolic pathways from reactions selected by the user. Having input the name of the starting compound (the ‘‘seed’’), the user is presented with a list of all reactions containing that compound and then selects the product of interest as the next point on the ensuing graph. The pathway diagram is then generated as the process iterates. A contextual menu is provided, which allows the user to (i) remove a compound from the graph, along with all associated links; (ii) search the reactions database again for additional reactions involving the compound and (iii) search for the compound within the Enzyme List.

Protein species – this term was originally introduced by Jungblut et al., 1996 – to name protein variants, which differ in their exact chemical composition. The term protein species differentiates between splicing variants, truncated proteins and posttranslational modified proteins. The exact chemical composition critically determines the function of a protein. Phosphorylation can activate or inactivate enzymatic activities. There are already many other posttranslational modifications, which are known to regulate enzymatic activities. Truncations also play an important role in activating enzymes. Therefore the knowledge of the identity comprising 100% sequence coverage and every posttranslational modification at its exact position is fundamental for assigning a function to an individual protein species. However, knowledge about the relationship of the function of a protein and its exact chemical composition is still not yet fully taken into account in many investigations of enzymes. In most of the proteomics approaches protein identification is based on sequence coverage significantly below 100% and posttranslational modifications are more or less ignored. Also in studies investigating single enzymes, a total analysis of the chemical structure of the enzyme of interest is not usually performed. Therefore it is recommended that this issue should be addressed in biochemical and biological investigations. The total analysis of the chemical composition of an enzyme is quite a big challenge; however it is even more challenging to develop strategies, which allow the validation of the correctness of the function–chemical composition relationship.

Metabolic Control Analysis (MCA) is a powerful quantitative framework for understanding and explaining the relationships between the global steady-state properties of a cellular system in terms of control coefficients, and the local properties of the individual components of the system in terms of elasticities. The elasticities are apparent kinetic orders, which derive directly from the kinetic properties of the enzymes. Since MCA relates elasticities to control coefficients through a matrix inversion, it allows one to predict and to quantify how the kinetics of individual enzymes affect the systemic behaviour of biological pathways. Most often this problem has been solved numerically, with algebraic and symbolic control analysis having been tackled less frequently. We present here a general implementation of the symbolic matrix inversion of MCA through symbolic algebraic computation. The algebraic expressions thus generated allow an in-depth analysis of where the control within a system lies and which parameters have the greatest effect on this control distribution, even if the exact values of the elasticities or control coefficients are unknown.

In previous contributions to the ESCEC proceedings we focused on the functionality of JWS Online and we made a comparison between JWS Online and other model database initiatives. In the current chapter an update is given on new developments for JWS Online and we illustrate the functionality of JWS Online web services in workflows.

The merging of mathematical models (either manually or assisted by computer programs) is an important requisite for creating large mathematical models of cells. A kinetic model describes biochemical quantities such as concentrations and reaction rates by explicit differential and algebraic equations. We can regard it as a list of model statements, each comprising a biochemical quantity (e. g. a substance concentration), the corresponding mathematical object (e. g. a variable or parameter), and a mathematical equation that makes it possible to compute its numerical value. When two such models are merged, typical conflicts have to be detected and resolved: (i) incompatible names or identifiers; (ii) incompatible physical units; (iii) duplicate elements with contradicting assignments; (iv) conflicting (‘‘semantically dependent’’) quantities; (v) cyclic dependencies between model equations. To define and judge whether merging algorithms are trustworthy, we need formal criteria for the validity of models; such criteria can be classified into the categories ‘‘syntax’’, ‘‘computation’’, ‘‘biochemical semantics’’, ‘‘physical laws and empirical knowledge’’ and ‘‘model relevance’’.